ABSTRACT
Weather forecasting is one of the most important and challenging field for scientists and engineers. The advent of technology has enabled us to obtain forecasts using complex mathematical models. For the last three decades, artificial intelligent based learning models like neural networks, genetic algorithms and neuro-fuzzy logic have shown much better results as compared to Box-Cox modeling approaches. Further accuracy is expectable by constructing a consortium of statistical and artificial intelligent methods. For weather forecasting, our trend is also towards the hybrid models. The accuracy of forecasting models can be made using different measures of assessments. In this paper, some hybrid methods are discussed with their merits and demerits. In this research we are going to use java for the front end designs and MySQL for the database.
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
The problem of how to assess the performance of weather forecasts has been a long-standing topic in meteorology. Since 1950, progress in numerical weather prediction (NWP) has prompted meteorologists to develop and apply many different objective verification and evaluation techniques to forecast fields. A comprehensive review and discussion of standard methods was compiled recently by Jolliffe and Stephenson (2003). Over the past two decades, the use of commercial weather forecast products by industrial decision makers has become much more widespread. In parallel the range of competing weather forecast services to choose from